A complete, reusable template for AI-enhanced development environments with local RAG, MCP integration, and visual service management.

This template provides a production-ready AI development ecosystem that you can deploy to any project in minutes:

• 🧠 Local RAG System - 2600+ document semantic search
• 🤖 MCP Server - AI agent integration protocol
• 📁 File Manager - Documentation upload/organization
• 🎛️ Control Panel - Visual service management
• ⚙️ App Control Scripts - Command-line orchestration

• ✅ Zero external dependencies - Works completely offline
• ✅ Project-agnostic - Adapts to any codebase
• ✅ AI-ready - Immediate integration with coding assistants
• ✅ Professional UI - Enterprise-grade control interfaces
• ✅ One-command setup - Automated deployment scripts

# Create new repo from template gh repo create my-ai-project --template hartou/ai-dev-toolkit-template # Deploy to existing project cd my-existing-project curl -sSL https://raw.githubusercontent.com/hartou/ai-dev-toolkit-template/main/scripts/deploy.sh | bash

# Install globally npm install -g ai-dev-toolkit # Deploy to any project cd my-project ai-dev-toolkit init ai-dev-toolkit start

# Clone template git clone https://github.com/hartou/ai-dev-toolkit-template.git cd ai-dev-toolkit-template # Configure and deploy ./scripts/setup.sh ./scripts/deploy.sh /path/to/target/project

ai-dev-toolkit-template/ ├── 📦 core/ # Core reusable components │ ├── rag-system/ # RAG engine + database │ ├── mcp-server/ # Model Context Protocol server │ ├── file-manager/ # Documentation management │ ├── control-panel/ # Service orchestration UI │ └── shared/ # Common utilities ├── 🔧 scripts/ # Automation and deployment │ ├── deploy.sh # Deploy to existing project │ ├── setup.sh # Initial configuration │ ├── app-control.sh # Service management │ └── templates/ # Configuration templates ├── 📋 configs/ # Default configurations │ ├── package.template.json # NPM dependencies │ ├── docker-compose.yml # Container deployment │ └── .env.template # Environment variables ├── 📚 docs/ # Documentation and guides └── 🧪 examples/ # Integration examples 

The toolkit automatically adapts to your project structure:

# Auto-detect project type ./scripts/detect-project.sh # Configure for specific frameworks ai-dev-toolkit configure --framework react ai-dev-toolkit configure --framework nextjs ai-dev-toolkit configure --framework python

# Index your project documentation ai-dev-toolkit index --source ./docs ai-dev-toolkit index --source ./README.md ai-dev-toolkit index --include "*.md,*.rst,*.txt"

# Customize service ports export RAG_PORT=4001 export MCP_PORT=4002 export FILE_MANAGER_PORT=4003 export CONTROL_PANEL_PORT=4004

Once deployed, manage your AI development environment:

# Start all services ./ai-control.sh start # Individual service management ./ai-control.sh rag start ./ai-control.sh mcp start ./ai-control.sh panel start # Web interfaces open http://localhost:4001 # RAG Search open http://localhost:4003 # File Manager open http://localhost:4004 # Control Panel

{ "settings": { "ai-dev-toolkit.ragEndpoint": "http://localhost:4001", "ai-dev-toolkit.mcpServer": "http://localhost:4002" } }

// Use the MCP server in your AI workflow import { MCPClient } from '@modelcontextprotocol/sdk' const mcp = new MCPClient('stdio', { command: 'node', args: ['./ai-dev-toolkit/mcp-server/index.js'] })

# GitHub Actions - name: Setup AI Dev Toolkit run: |  curl -sSL https://get.ai-dev-toolkit.com | bash  ai-dev-toolkit start --headless   - name: AI-Enhanced Testing  run: |  ai-dev-toolkit query "test patterns for React components"  npm test

• 🚀 Setup Time: < 2 minutes
• 💾 Memory Usage: < 200MB total
• 🔍 Search Speed: < 100ms per query
• 📄 Document Capacity: 10,000+ documents
• ⚡ Cold Start: < 5 seconds

Each component runs independently and can be deployed separately:

graph TB A[Control Panel :4004] --> B[RAG System :4001] A --> C[MCP Server :4002] A --> D[File Manager :4003] B --> E[SQLite Database] C --> E D --> F[Documentation Store] G[AI Agents] --> C H[Developers] --> A 

1. Documentation → File Manager → RAG Indexer → SQLite Database
2. AI Queries → MCP Server → RAG Engine → Semantic Search Results
3. Human Queries → Web Interface → RAG API → Search Results
4. Management → Control Panel → Service Scripts → System Status

• Personal project knowledge bases
• AI-assisted coding environments
• Documentation-driven development

• Shared knowledge repositories
• Team onboarding acceleration
• Collaborative AI development

• Enterprise AI development platforms
• Standardized development environments
• Knowledge management systems

• Contributor onboarding
• Documentation accessibility
• AI-powered issue resolution

Keep your toolkit current:

# Update to latest version ai-dev-toolkit update # Update specific components ai-dev-toolkit update rag ai-dev-toolkit update mcp # Backup before updates ai-dev-toolkit backup ai-dev-toolkit restore backup-20241109.zip

• 🔌 Plugin System - Custom integrations
• 🌐 Multi-language Support - Python, Java, Go
• 📱 Mobile Interface - Responsive design
• 🔒 Authentication - Multi-user support
• ☁️ Cloud Deployment - Kubernetes manifests

• 🤖 AI Code Generation - Integrated with RAG knowledge
• 📊 Analytics Dashboard - Usage insights and optimization
• 🔄 Real-time Collaboration - Live document sharing
• 🎨 Custom Themes - Branding and personalization

Help improve the toolkit:

# Development setup git clone https://github.com/hartou/ai-dev-toolkit-template.git cd ai-dev-toolkit-template ./scripts/dev-setup.sh # Run tests npm test # Submit improvements git checkout -b feature/my-improvement git commit -m "Add awesome feature" git push origin feature/my-improvement

MIT License - Use freely in personal and commercial projects.

• 📧 Email: support@ai-dev-toolkit.com
• 💬 Discord: AI Dev Toolkit Community
• 🐛 Issues: GitHub Issues
• 📖 Docs: Full Documentation

Transform any project into an AI-enhanced development environment in under 2 minutes. 🚀